| 1. | Course Title | Data Science and Machine Learning | |||||||||||
| 2. | Code | 4ФЕИТ07012 | |||||||||||
| 3. | Study program | 6-ARSI,13-PMA, 20-IMSA, 21-PNMI | |||||||||||
| 4. | Organizer of the study program (unit, institute, department) | Faculty of Electrical Engineering and Information Technologies | |||||||||||
| 5. | Degree (first, second, third cycle) | Second cycle | |||||||||||
| 6. | Academic year/semester | I/1 | 7. | Number of ECTS credits | 6.00 | ||||||||
| 8. | Lecturer | Dr Hristijan Gjoreski | |||||||||||
| 9. | Course Prerequisites | ||||||||||||
| 10. | Course Goals (acquired competencies):
Enriching the knowledge in Data Science and Machine Intelligence. With this course, the student will gain knowledge for practical use of tools and software for data processing, ML model development, evaluation and comparison of results. |
||||||||||||
| 11. | Course Syllabus:
Studying data science and data analysis with machine learning algorithms. Analyzing data sets with different algorithms: Decision trees, K nearest neighbors, Naive Bayes, Random Forests, SVM, Ensemble Models, XGBoost, Gradient Boost. Development of classification and regression models, clustering of data, visualization of data and models, as well as analysis and comparison of different types of evaluation of built models. Implementation and evaluation of algorithms and models using the Java and Python environment. |
||||||||||||
| 12. | Learning methods:
Lectures, auditory and laboratory exercises, independent learning, independent work on project tasks |
||||||||||||
| 13. | Total number of course hours | 180 | |||||||||||
| 14. | Distribution of course hours | 3 + 3 | |||||||||||
| 15. | Forms of teaching | 15.1 | Lectures-theoretical teaching | 45 hours | |||||||||
| 15.2 | Exercises (laboratory, practice classes), seminars, teamwork | 45 hours | |||||||||||
| 16. | Other course activities | 16.1 | Projects, seminar papers | 30 hours | |||||||||
| 16.2 | Individual tasks | 30 hours | |||||||||||
| 16.3 | Homework and self-learning | 30 hours | |||||||||||
| 17. | Grading | ||||||||||||
| 17.1 | Exams | points | |||||||||||
| 17.2 | Seminar work/project (presentation: written and oral) | 50 points | |||||||||||
| 17.3. | Activity and participation | points | |||||||||||
| 17.4. | Final exam | 50 points | |||||||||||
| 18. | Grading criteria (points) | up to 50 points | 5 (five) (F) | ||||||||||
| from 51 to 60 points | 6 (six) (E) | ||||||||||||
| from 61 to 70 points | 7 (seven) (D) | ||||||||||||
| from 71 to 80 points | 8 (eight) (C) | ||||||||||||
| from 81 to 90 points | 9 (nine) (B) | ||||||||||||
| from 91 to 100 points | 10 (ten) (A) | ||||||||||||
| 19. | Conditions for acquiring teacher’s signature and for taking final exam | Regular attendance on lectures | |||||||||||
| 20. | Forms of assessment | Project assignment and final exam. | |||||||||||
| 21. | Language | Macedonian and English | |||||||||||
| 22. | Method of monitoring of teaching quality | Self-evaluation | |||||||||||
| 23. | Literature | ||||||||||||
| 23.1. | Required Literature | ||||||||||||
| No. | Author | Title | Publisher | Year | |||||||||
| 1. | Aurélien Géron | Hands-On Machine Learning with Scikit-Learn and TensorFlow | O’Reilly Media; 1st edition (April 25, 2017) | 2017 | |||||||||
